{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze Big Financial Data**\n",
    "\n",
    "O'Reilly (2014)\n",
    "\n",
    "Yves Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"border:0px solid grey;\" src=\"http://hilpisch.com/python_for_finance.png\" alt=\"Python for Finance\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Buy the book ** |\n",
    "<a href='http://shop.oreilly.com/product/0636920032441.do' target='_blank'>O'Reilly</a> |\n",
    "<a href='http://www.amazon.com/Yves-Hilpisch/e/B00JCYHHJM' target='_blank'>Amazon</a>\n",
    "\n",
    "**All book codes & IPYNBs** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**The Python Quants GmbH** | <a href='http://tpq.io' target='_blank'>http://tpq.io</a>\n",
    "\n",
    "**Contact us** | <a href='mailto:pff@tpq.io'>pff@tpq.io</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Financial Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'serif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "uuid": "eda2a742-134d-4d47-8b30-557b846b9bb3"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Steps with DataFrame Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "uuid": "f3be2d89-829a-49b2-96fc-07c475db1e3f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers\n",
       "a       10\n",
       "b       20\n",
       "c       30\n",
       "d       40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([10, 20, 30, 40], columns=['numbers'],\n",
    "                  index=['a', 'b', 'c', 'd'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "uuid": "47b70a7b-710f-4c40-9a70-b09db7af1a12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index  # the index values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "uuid": "a36c6695-520d-4df1-a6fa-5f8362af37a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['numbers'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns  # the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "c93aed37-21de-429d-86ed-9849e4c3e23c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numbers    30\n",
       "Name: c, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['c']  # selection via index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "8c7c2f69-3673-40d9-a568-0471c629810d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   numbers\n",
       "a       10\n",
       "d       40"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['a', 'd']]  # selection of multiple indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "uuid": "c3ce0cc3-26e8-4256-ab8c-9a2d4b181633"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers\n",
       "b       20\n",
       "c       30"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.index[1:3]]  # selection via Index object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "uuid": "94b1d846-63df-49f4-8a7f-8fed03e5f4fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numbers    100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()  # sum per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "uuid": "4e73eb4f-352d-4527-b0c5-4f3a6e7eb354"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "   numbers\n",
       "a      100\n",
       "b      400\n",
       "c      900\n",
       "d     1600"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x ** 2)  # square of every element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "uuid": "75206a83-0154-4be2-88d0-7a82a190fda1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>900</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1600</td>\n",
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      "text/plain": [
       "   numbers\n",
       "a      100\n",
       "b      400\n",
       "c      900\n",
       "d     1600"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df ** 2  # again square, this time NumPy-like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "uuid": "49a2633a-b3c0-4d00-a227-e0ff4a8cf81d"
   },
   "outputs": [
    {
     "data": {
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       "      <th>floats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20</td>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>30</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>40</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers  floats\n",
       "a       10     1.5\n",
       "b       20     2.5\n",
       "c       30     3.5\n",
       "d       40     4.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['floats'] = (1.5, 2.5, 3.5, 4.5)\n",
    "  # new column is generated\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "uuid": "c49b9aea-417a-4c2b-8e27-0e8771a77c87"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.5\n",
       "b    2.5\n",
       "c    3.5\n",
       "d    4.5\n",
       "Name: floats, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['floats']  # selection of column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "uuid": "aa892c41-6637-45ed-876b-6a70285e4c0b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Guido</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>30</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Francesc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>40</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Yves</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   numbers  floats     names\n",
       "a       10     1.5     Guido\n",
       "b       20     2.5     Felix\n",
       "c       30     3.5  Francesc\n",
       "d       40     4.5      Yves"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['names'] = pd.DataFrame(['Yves', 'Guido', 'Felix', 'Francesc'],\n",
    "                           index=['d', 'a', 'b', 'c'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "uuid": "584ac18c-161f-4c7b-8ff1-1cd406fb8437"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Francesc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Yves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>5.75</td>\n",
       "      <td>Henry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers  floats     names\n",
       "0       10    1.50     Guido\n",
       "1       20    2.50     Felix\n",
       "2       30    3.50  Francesc\n",
       "3       40    4.50      Yves\n",
       "4      100    5.75     Henry"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append({'numbers': 100, 'floats': 5.75, 'names': 'Henry'},\n",
    "               ignore_index=True)\n",
    "  # temporary object; df not changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "uuid": "9068cd04-c6ff-4d0c-bd52-cf04cd89a0e9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>floats</th>\n",
       "      <th>names</th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Guido</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.50</td>\n",
       "      <td>Felix</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.50</td>\n",
       "      <td>Francesc</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.50</td>\n",
       "      <td>Yves</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>5.75</td>\n",
       "      <td>Henry</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floats     names  numbers\n",
       "a    1.50     Guido       10\n",
       "b    2.50     Felix       20\n",
       "c    3.50  Francesc       30\n",
       "d    4.50      Yves       40\n",
       "z    5.75     Henry      100"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.append(pd.DataFrame({'numbers': 100, 'floats': 5.75,\n",
    "                             'names': 'Henry'}, index=['z',]))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "uuid": "0d526ac2-8691-4a59-83c5-712c800f0464"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>floats</th>\n",
       "      <th>names</th>\n",
       "      <th>numbers</th>\n",
       "      <th>squares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Guido</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.50</td>\n",
       "      <td>Felix</td>\n",
       "      <td>20</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.50</td>\n",
       "      <td>Francesc</td>\n",
       "      <td>30</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.50</td>\n",
       "      <td>Yves</td>\n",
       "      <td>40</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>5.75</td>\n",
       "      <td>Henry</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floats     names  numbers  squares\n",
       "a    1.50     Guido       10      1.0\n",
       "b    2.50     Felix       20      4.0\n",
       "c    3.50  Francesc       30      9.0\n",
       "d    4.50      Yves       40     16.0\n",
       "z    5.75     Henry      100      NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.join(pd.DataFrame([1, 4, 9, 16, 25],\n",
    "            index=['a', 'b', 'c', 'd', 'y'],\n",
    "            columns=['squares',]))\n",
    "  # temporary object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "uuid": "6558c7ff-f24e-4dd0-b57e-a30196bad1f4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>floats</th>\n",
       "      <th>names</th>\n",
       "      <th>numbers</th>\n",
       "      <th>squares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Guido</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.50</td>\n",
       "      <td>Felix</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.50</td>\n",
       "      <td>Francesc</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.50</td>\n",
       "      <td>Yves</td>\n",
       "      <td>40.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>5.75</td>\n",
       "      <td>Henry</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floats     names  numbers  squares\n",
       "a    1.50     Guido     10.0      1.0\n",
       "b    2.50     Felix     20.0      4.0\n",
       "c    3.50  Francesc     30.0      9.0\n",
       "d    4.50      Yves     40.0     16.0\n",
       "y     NaN       NaN      NaN     25.0\n",
       "z    5.75     Henry    100.0      NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.join(pd.DataFrame([1, 4, 9, 16, 25],\n",
    "                    index=['a', 'b', 'c', 'd', 'y'],\n",
    "                    columns=['squares',]),\n",
    "                    how='outer')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "uuid": "3e863c7f-7875-4911-997b-6e48123dc1e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numbers    40.0\n",
       "squares    11.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['numbers', 'squares']].mean()\n",
    "  # column-wise mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "uuid": "c52173a0-485d-4eb2-b6b4-407d1ff2c30e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numbers    35.355339\n",
       "squares     9.669540\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['numbers', 'squares']].std()\n",
    "  # column-wise standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Steps with DataFrame Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "uuid": "d6f56a00-91e6-4221-a1ec-6093f416d1be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.321677, -0.223755,  0.845289,  1.312106],\n",
       "       [-0.958205,  0.763066, -0.188933, -0.863822],\n",
       "       [ 0.224055, -0.62577 , -1.975839,  0.250876],\n",
       "       [ 0.574035, -0.182031,  0.312119,  0.597762],\n",
       "       [ 0.172791,  1.04793 , -0.687687,  0.4307  ],\n",
       "       [ 0.546069,  1.035873, -0.371233,  1.692606],\n",
       "       [-1.600886, -0.179964,  0.376877,  0.27294 ],\n",
       "       [ 1.265513,  1.319254,  1.26514 ,  1.596943],\n",
       "       [-1.668441,  0.36277 ,  0.710947,  2.149087]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.standard_normal((9, 4))\n",
    "a.round(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "uuid": "450bd14d-7668-4f3f-a863-966f13562818"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.321677</td>\n",
       "      <td>-0.223755</td>\n",
       "      <td>0.845289</td>\n",
       "      <td>1.312106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.958205</td>\n",
       "      <td>0.763066</td>\n",
       "      <td>-0.188933</td>\n",
       "      <td>-0.863822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.224055</td>\n",
       "      <td>-0.625770</td>\n",
       "      <td>-1.975839</td>\n",
       "      <td>0.250876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.574035</td>\n",
       "      <td>-0.182031</td>\n",
       "      <td>0.312119</td>\n",
       "      <td>0.597762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.172791</td>\n",
       "      <td>1.047930</td>\n",
       "      <td>-0.687687</td>\n",
       "      <td>0.430700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.546069</td>\n",
       "      <td>1.035873</td>\n",
       "      <td>-0.371233</td>\n",
       "      <td>1.692606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.600886</td>\n",
       "      <td>-0.179964</td>\n",
       "      <td>0.376877</td>\n",
       "      <td>0.272940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.265513</td>\n",
       "      <td>1.319254</td>\n",
       "      <td>1.265140</td>\n",
       "      <td>1.596943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.668441</td>\n",
       "      <td>0.362770</td>\n",
       "      <td>0.710947</td>\n",
       "      <td>2.149087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  1.321677 -0.223755  0.845289  1.312106\n",
       "1 -0.958205  0.763066 -0.188933 -0.863822\n",
       "2  0.224055 -0.625770 -1.975839  0.250876\n",
       "3  0.574035 -0.182031  0.312119  0.597762\n",
       "4  0.172791  1.047930 -0.687687  0.430700\n",
       "5  0.546069  1.035873 -0.371233  1.692606\n",
       "6 -1.600886 -0.179964  0.376877  0.272940\n",
       "7  1.265513  1.319254  1.265140  1.596943\n",
       "8 -1.668441  0.362770  0.710947  2.149087"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(a)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "uuid": "968395a4-12bc-46d2-b486-6c767abce366"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.321677</td>\n",
       "      <td>-0.223755</td>\n",
       "      <td>0.845289</td>\n",
       "      <td>1.312106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.958205</td>\n",
       "      <td>0.763066</td>\n",
       "      <td>-0.188933</td>\n",
       "      <td>-0.863822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.224055</td>\n",
       "      <td>-0.625770</td>\n",
       "      <td>-1.975839</td>\n",
       "      <td>0.250876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.574035</td>\n",
       "      <td>-0.182031</td>\n",
       "      <td>0.312119</td>\n",
       "      <td>0.597762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.172791</td>\n",
       "      <td>1.047930</td>\n",
       "      <td>-0.687687</td>\n",
       "      <td>0.430700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.546069</td>\n",
       "      <td>1.035873</td>\n",
       "      <td>-0.371233</td>\n",
       "      <td>1.692606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.600886</td>\n",
       "      <td>-0.179964</td>\n",
       "      <td>0.376877</td>\n",
       "      <td>0.272940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.265513</td>\n",
       "      <td>1.319254</td>\n",
       "      <td>1.265140</td>\n",
       "      <td>1.596943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.668441</td>\n",
       "      <td>0.362770</td>\n",
       "      <td>0.710947</td>\n",
       "      <td>2.149087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        No1       No2       No3       No4\n",
       "0  1.321677 -0.223755  0.845289  1.312106\n",
       "1 -0.958205  0.763066 -0.188933 -0.863822\n",
       "2  0.224055 -0.625770 -1.975839  0.250876\n",
       "3  0.574035 -0.182031  0.312119  0.597762\n",
       "4  0.172791  1.047930 -0.687687  0.430700\n",
       "5  0.546069  1.035873 -0.371233  1.692606\n",
       "6 -1.600886 -0.179964  0.376877  0.272940\n",
       "7  1.265513  1.319254  1.265140  1.596943\n",
       "8 -1.668441  0.362770  0.710947  2.149087"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['No1', 'No2', 'No3', 'No4']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "uuid": "68e8d73f-93d3-47ac-a656-1edbdebcd1ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.18203051098590636"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['No2'].iloc[3]  # value in column No2 at index position 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "uuid": "a80e1e88-d211-4ee4-a6d3-90403a7739a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-31', '2015-02-28', '2015-03-31', '2015-04-30',\n",
       "               '2015-05-31', '2015-06-30', '2015-07-31', '2015-08-31',\n",
       "               '2015-09-30'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2015-1-1', periods=9, freq='M')\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "uuid": "d8fef9ed-25ca-4ae0-bd0c-026d340a903b"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>1.321677</td>\n",
       "      <td>-0.223755</td>\n",
       "      <td>0.845289</td>\n",
       "      <td>1.312106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>-0.958205</td>\n",
       "      <td>0.763066</td>\n",
       "      <td>-0.188933</td>\n",
       "      <td>-0.863822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>0.224055</td>\n",
       "      <td>-0.625770</td>\n",
       "      <td>-1.975839</td>\n",
       "      <td>0.250876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>0.574035</td>\n",
       "      <td>-0.182031</td>\n",
       "      <td>0.312119</td>\n",
       "      <td>0.597762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>0.172791</td>\n",
       "      <td>1.047930</td>\n",
       "      <td>-0.687687</td>\n",
       "      <td>0.430700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>0.546069</td>\n",
       "      <td>1.035873</td>\n",
       "      <td>-0.371233</td>\n",
       "      <td>1.692606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>-1.600886</td>\n",
       "      <td>-0.179964</td>\n",
       "      <td>0.376877</td>\n",
       "      <td>0.272940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>1.265513</td>\n",
       "      <td>1.319254</td>\n",
       "      <td>1.265140</td>\n",
       "      <td>1.596943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>-1.668441</td>\n",
       "      <td>0.362770</td>\n",
       "      <td>0.710947</td>\n",
       "      <td>2.149087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 No1       No2       No3       No4\n",
       "2015-01-31  1.321677 -0.223755  0.845289  1.312106\n",
       "2015-02-28 -0.958205  0.763066 -0.188933 -0.863822\n",
       "2015-03-31  0.224055 -0.625770 -1.975839  0.250876\n",
       "2015-04-30  0.574035 -0.182031  0.312119  0.597762\n",
       "2015-05-31  0.172791  1.047930 -0.687687  0.430700\n",
       "2015-06-30  0.546069  1.035873 -0.371233  1.692606\n",
       "2015-07-31 -1.600886 -0.179964  0.376877  0.272940\n",
       "2015-08-31  1.265513  1.319254  1.265140  1.596943\n",
       "2015-09-30 -1.668441  0.362770  0.710947  2.149087"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = dates\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "uuid": "bcc38d60-3e1c-49bb-b883-ea7564c136b4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.321677, -0.223755,  0.845289,  1.312106],\n",
       "       [-0.958205,  0.763066, -0.188933, -0.863822],\n",
       "       [ 0.224055, -0.62577 , -1.975839,  0.250876],\n",
       "       [ 0.574035, -0.182031,  0.312119,  0.597762],\n",
       "       [ 0.172791,  1.04793 , -0.687687,  0.4307  ],\n",
       "       [ 0.546069,  1.035873, -0.371233,  1.692606],\n",
       "       [-1.600886, -0.179964,  0.376877,  0.27294 ],\n",
       "       [ 1.265513,  1.319254,  1.26514 ,  1.596943],\n",
       "       [-1.668441,  0.36277 ,  0.710947,  2.149087]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df).round(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "uuid": "f760ea25-c64c-4e70-9f91-b72701d919ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No1   -0.123391\n",
       "No2    3.317374\n",
       "No3    0.286681\n",
       "No4    7.439198\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "uuid": "3dd9bd77-eb80-46cb-87f3-62c053a8e223"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No1   -0.013710\n",
       "No2    0.368597\n",
       "No3    0.031853\n",
       "No4    0.826578\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "uuid": "8e167ea8-09b7-4585-8cac-28fe20eefe66"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>1.321677</td>\n",
       "      <td>-0.223755</td>\n",
       "      <td>0.845289</td>\n",
       "      <td>1.312106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>0.363472</td>\n",
       "      <td>0.539312</td>\n",
       "      <td>0.656356</td>\n",
       "      <td>0.448284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>0.587528</td>\n",
       "      <td>-0.086458</td>\n",
       "      <td>-1.319483</td>\n",
       "      <td>0.699160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>1.161563</td>\n",
       "      <td>-0.268489</td>\n",
       "      <td>-1.007364</td>\n",
       "      <td>1.296922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>1.334353</td>\n",
       "      <td>0.779441</td>\n",
       "      <td>-1.695050</td>\n",
       "      <td>1.727622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>1.880423</td>\n",
       "      <td>1.815314</td>\n",
       "      <td>-2.066284</td>\n",
       "      <td>3.420228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>0.279537</td>\n",
       "      <td>1.635350</td>\n",
       "      <td>-1.689407</td>\n",
       "      <td>3.693167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>1.545050</td>\n",
       "      <td>2.954604</td>\n",
       "      <td>-0.424267</td>\n",
       "      <td>5.290110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>-0.123391</td>\n",
       "      <td>3.317374</td>\n",
       "      <td>0.286681</td>\n",
       "      <td>7.439198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 No1       No2       No3       No4\n",
       "2015-01-31  1.321677 -0.223755  0.845289  1.312106\n",
       "2015-02-28  0.363472  0.539312  0.656356  0.448284\n",
       "2015-03-31  0.587528 -0.086458 -1.319483  0.699160\n",
       "2015-04-30  1.161563 -0.268489 -1.007364  1.296922\n",
       "2015-05-31  1.334353  0.779441 -1.695050  1.727622\n",
       "2015-06-30  1.880423  1.815314 -2.066284  3.420228\n",
       "2015-07-31  0.279537  1.635350 -1.689407  3.693167\n",
       "2015-08-31  1.545050  2.954604 -0.424267  5.290110\n",
       "2015-09-30 -0.123391  3.317374  0.286681  7.439198"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "uuid": "125980cc-91ec-4ab4-9a4a-cfd772dd1254"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.013710</td>\n",
       "      <td>0.368597</td>\n",
       "      <td>0.031853</td>\n",
       "      <td>0.826578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.135733</td>\n",
       "      <td>0.699097</td>\n",
       "      <td>0.974758</td>\n",
       "      <td>0.937597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.668441</td>\n",
       "      <td>-0.625770</td>\n",
       "      <td>-1.975839</td>\n",
       "      <td>-0.863822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.958205</td>\n",
       "      <td>-0.182031</td>\n",
       "      <td>-0.371233</td>\n",
       "      <td>0.272940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.224055</td>\n",
       "      <td>0.362770</td>\n",
       "      <td>0.312119</td>\n",
       "      <td>0.597762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.574035</td>\n",
       "      <td>1.035873</td>\n",
       "      <td>0.710947</td>\n",
       "      <td>1.596943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.321677</td>\n",
       "      <td>1.319254</td>\n",
       "      <td>1.265140</td>\n",
       "      <td>2.149087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            No1       No2       No3       No4\n",
       "count  9.000000  9.000000  9.000000  9.000000\n",
       "mean  -0.013710  0.368597  0.031853  0.826578\n",
       "std    1.135733  0.699097  0.974758  0.937597\n",
       "min   -1.668441 -0.625770 -1.975839 -0.863822\n",
       "25%   -0.958205 -0.182031 -0.371233  0.272940\n",
       "50%    0.224055  0.362770  0.312119  0.597762\n",
       "75%    0.574035  1.035873  0.710947  1.596943\n",
       "max    1.321677  1.319254  1.265140  2.149087"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "uuid": "9dfc1e40-c030-4a9c-9e3a-ff28c64a93df"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>1.149642</td>\n",
       "      <td>0.473027</td>\n",
       "      <td>0.919396</td>\n",
       "      <td>1.145472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>0.978879</td>\n",
       "      <td>0.873537</td>\n",
       "      <td>0.434664</td>\n",
       "      <td>0.929420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>0.473345</td>\n",
       "      <td>0.791056</td>\n",
       "      <td>1.405645</td>\n",
       "      <td>0.500875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>0.757651</td>\n",
       "      <td>0.426650</td>\n",
       "      <td>0.558676</td>\n",
       "      <td>0.773150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>0.415681</td>\n",
       "      <td>1.023685</td>\n",
       "      <td>0.829269</td>\n",
       "      <td>0.656277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>0.738965</td>\n",
       "      <td>1.017778</td>\n",
       "      <td>0.609289</td>\n",
       "      <td>1.301002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>1.265261</td>\n",
       "      <td>0.424221</td>\n",
       "      <td>0.613903</td>\n",
       "      <td>0.522436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>1.124950</td>\n",
       "      <td>1.148588</td>\n",
       "      <td>1.124784</td>\n",
       "      <td>1.263702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>1.291681</td>\n",
       "      <td>0.602304</td>\n",
       "      <td>0.843177</td>\n",
       "      <td>1.465977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 No1       No2       No3       No4\n",
       "2015-01-31  1.149642  0.473027  0.919396  1.145472\n",
       "2015-02-28  0.978879  0.873537  0.434664  0.929420\n",
       "2015-03-31  0.473345  0.791056  1.405645  0.500875\n",
       "2015-04-30  0.757651  0.426650  0.558676  0.773150\n",
       "2015-05-31  0.415681  1.023685  0.829269  0.656277\n",
       "2015-06-30  0.738965  1.017778  0.609289  1.301002\n",
       "2015-07-31  1.265261  0.424221  0.613903  0.522436\n",
       "2015-08-31  1.124950  1.148588  1.124784  1.263702\n",
       "2015-09-30  1.291681  0.602304  0.843177  1.465977"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(abs(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "uuid": "a540362b-50d7-4ef0-89ba-0b6ee38033f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No1    8.196056\n",
       "No2    6.780846\n",
       "No3    7.338804\n",
       "No4    8.558312\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(abs(df)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "uuid": "4b1834ec-9f9b-41d6-8d06-f2efc8433dc4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1080f5f28>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NN+rf4uP2z5CRSdQYqCq+mSW5C1Apr+3FpbnTyU82HKLP4SMrJYGv31tJZsrE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\nBMEAJwx5EATBACcMeRAEwQDnv4v0TRUepTWfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1080eb780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df.cumsum().plot(lw=2.0, grid=True)\n",
    "# tag: dataframe_plot\n",
    "# title: Line plot of a DataFrame object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "uuid": "e86f82d1-5934-42d3-a986-f01bc829adaa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "uuid": "bcebc814-623d-4e8a-81e9-314ab36a7429"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-31    1.321677\n",
       "2015-02-28   -0.958205\n",
       "2015-03-31    0.224055\n",
       "2015-04-30    0.574035\n",
       "2015-05-31    0.172791\n",
       "2015-06-30    0.546069\n",
       "2015-07-31   -1.600886\n",
       "2015-08-31    1.265513\n",
       "2015-09-30   -1.668441\n",
       "Freq: M, Name: No1, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['No1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "uuid": "ca241ef9-5359-4c89-bc92-be6346cb3959"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['No1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "uuid": "b3d4cc90-e499-459c-88a5-011fde80d864"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'value')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fJMP+FwMWAO78j3tUfE1ExisvQkWFy9GkluCnH5P15OOYgQDlDzzi2cVEYsfE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\nc2bLK6I/eQ8A/29+TdHOqT+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aEF7uiwU+0FpPst/MegO/BrKVUvE7ye+AokbHf2N/XAn0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107f182e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df['No1'].cumsum().plot(style='r', lw=2., grid=True)\n",
    "plt.xlabel('date')\n",
    "plt.ylabel('value')\n",
    "# tag: time_series\n",
    "# title: Line plot of a Series object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GroupBy Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true,
    "uuid": "4bc106dd-9590-4566-bc70-d410517c8223"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "      <th>Quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>1.321677</td>\n",
       "      <td>-0.223755</td>\n",
       "      <td>0.845289</td>\n",
       "      <td>1.312106</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>-0.958205</td>\n",
       "      <td>0.763066</td>\n",
       "      <td>-0.188933</td>\n",
       "      <td>-0.863822</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>0.224055</td>\n",
       "      <td>-0.625770</td>\n",
       "      <td>-1.975839</td>\n",
       "      <td>0.250876</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>0.574035</td>\n",
       "      <td>-0.182031</td>\n",
       "      <td>0.312119</td>\n",
       "      <td>0.597762</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>0.172791</td>\n",
       "      <td>1.047930</td>\n",
       "      <td>-0.687687</td>\n",
       "      <td>0.430700</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>0.546069</td>\n",
       "      <td>1.035873</td>\n",
       "      <td>-0.371233</td>\n",
       "      <td>1.692606</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>-1.600886</td>\n",
       "      <td>-0.179964</td>\n",
       "      <td>0.376877</td>\n",
       "      <td>0.272940</td>\n",
       "      <td>Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>1.265513</td>\n",
       "      <td>1.319254</td>\n",
       "      <td>1.265140</td>\n",
       "      <td>1.596943</td>\n",
       "      <td>Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>-1.668441</td>\n",
       "      <td>0.362770</td>\n",
       "      <td>0.710947</td>\n",
       "      <td>2.149087</td>\n",
       "      <td>Q3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 No1       No2       No3       No4 Quarter\n",
       "2015-01-31  1.321677 -0.223755  0.845289  1.312106      Q1\n",
       "2015-02-28 -0.958205  0.763066 -0.188933 -0.863822      Q1\n",
       "2015-03-31  0.224055 -0.625770 -1.975839  0.250876      Q1\n",
       "2015-04-30  0.574035 -0.182031  0.312119  0.597762      Q2\n",
       "2015-05-31  0.172791  1.047930 -0.687687  0.430700      Q2\n",
       "2015-06-30  0.546069  1.035873 -0.371233  1.692606      Q2\n",
       "2015-07-31 -1.600886 -0.179964  0.376877  0.272940      Q3\n",
       "2015-08-31  1.265513  1.319254  1.265140  1.596943      Q3\n",
       "2015-09-30 -1.668441  0.362770  0.710947  2.149087      Q3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quarter'] = ['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2', 'Q3', 'Q3', 'Q3']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = df.groupby('Quarter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "uuid": "804e567f-6b74-4405-a10e-d19d914655e7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <td>0.195843</td>\n",
       "      <td>-0.028819</td>\n",
       "      <td>-0.439828</td>\n",
       "      <td>0.233053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>0.430965</td>\n",
       "      <td>0.633924</td>\n",
       "      <td>-0.248934</td>\n",
       "      <td>0.907023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3</th>\n",
       "      <td>-0.667938</td>\n",
       "      <td>0.500687</td>\n",
       "      <td>0.784321</td>\n",
       "      <td>1.339657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              No1       No2       No3       No4\n",
       "Quarter                                        \n",
       "Q1       0.195843 -0.028819 -0.439828  0.233053\n",
       "Q2       0.430965  0.633924 -0.248934  0.907023\n",
       "Q3      -0.667938  0.500687  0.784321  1.339657"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "uuid": "7eb45e5c-b86f-4464-afd9-d5a3665e0f8e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <td>1.321677</td>\n",
       "      <td>0.763066</td>\n",
       "      <td>0.845289</td>\n",
       "      <td>1.312106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>0.574035</td>\n",
       "      <td>1.047930</td>\n",
       "      <td>0.312119</td>\n",
       "      <td>1.692606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3</th>\n",
       "      <td>1.265513</td>\n",
       "      <td>1.319254</td>\n",
       "      <td>1.265140</td>\n",
       "      <td>2.149087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              No1       No2       No3       No4\n",
       "Quarter                                        \n",
       "Q1       1.321677  0.763066  0.845289  1.312106\n",
       "Q2       0.574035  1.047930  0.312119  1.692606\n",
       "Q3       1.265513  1.319254  1.265140  2.149087"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "uuid": "a871b95e-5946-4b09-b8dc-bc9503d2ff14"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quarter\n",
       "Q1    3\n",
       "Q2    3\n",
       "Q3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "uuid": "542cf99a-bbf8-447e-9643-d6887ac74be7"
   },
   "outputs": [],
   "source": [
    "df['Odd_Even'] = ['Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even',\n",
    "                  'Odd', 'Even', 'Odd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "uuid": "f5144c9f-ff37-4e35-9417-e39debdcd45b"
   },
   "outputs": [],
   "source": [
    "groups = df.groupby(['Quarter', 'Odd_Even'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "uuid": "06904508-dbf1-431f-a3a2-681f29f03c51"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quarter  Odd_Even\n",
       "Q1       Even        1\n",
       "         Odd         2\n",
       "Q2       Even        2\n",
       "         Odd         1\n",
       "Q3       Even        1\n",
       "         Odd         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "uuid": "b8471956-40fc-4203-a54a-aaa45f5a3c00"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <th>Odd_Even</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Q1</th>\n",
       "      <th>Even</th>\n",
       "      <td>-0.958205</td>\n",
       "      <td>0.763066</td>\n",
       "      <td>-0.188933</td>\n",
       "      <td>-0.863822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odd</th>\n",
       "      <td>0.772866</td>\n",
       "      <td>-0.424762</td>\n",
       "      <td>-0.565275</td>\n",
       "      <td>0.781491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Q2</th>\n",
       "      <th>Even</th>\n",
       "      <td>0.560052</td>\n",
       "      <td>0.426921</td>\n",
       "      <td>-0.029557</td>\n",
       "      <td>1.145184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odd</th>\n",
       "      <td>0.172791</td>\n",
       "      <td>1.047930</td>\n",
       "      <td>-0.687687</td>\n",
       "      <td>0.430700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Q3</th>\n",
       "      <th>Even</th>\n",
       "      <td>1.265513</td>\n",
       "      <td>1.319254</td>\n",
       "      <td>1.265140</td>\n",
       "      <td>1.596943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odd</th>\n",
       "      <td>-1.634663</td>\n",
       "      <td>0.091403</td>\n",
       "      <td>0.543912</td>\n",
       "      <td>1.211014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       No1       No2       No3       No4\n",
       "Quarter Odd_Even                                        \n",
       "Q1      Even     -0.958205  0.763066 -0.188933 -0.863822\n",
       "        Odd       0.772866 -0.424762 -0.565275  0.781491\n",
       "Q2      Even      0.560052  0.426921 -0.029557  1.145184\n",
       "        Odd       0.172791  1.047930 -0.687687  0.430700\n",
       "Q3      Even      1.265513  1.319254  1.265140  1.596943\n",
       "        Odd      -1.634663  0.091403  0.543912  1.211014"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "uuid": "53a33e39-a3ff-4c95-b0f2-a94d727ae0da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1972 entries, 2010-01-04 to 2017-10-31\n",
      "Data columns (total 12 columns):\n",
      "AAPL.O    1972 non-null float64\n",
      "MSFT.O    1972 non-null float64\n",
      "INTC.O    1972 non-null float64\n",
      "AMZN.O    1972 non-null float64\n",
      "GS.N      1972 non-null float64\n",
      "SPY       1972 non-null float64\n",
      ".SPX      1972 non-null float64\n",
      ".VIX      1972 non-null float64\n",
      "EUR=      1972 non-null float64\n",
      "XAU=      1972 non-null float64\n",
      "GDX       1972 non-null float64\n",
      "GLD       1972 non-null float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 200.3 KB\n"
     ]
    }
   ],
   "source": [
    "# data from Thomson Reuters Eikon API\n",
    "raw = pd.read_csv('source/tr_eikon_eod_data.csv',\n",
    "                 index_col=0, parse_dates=True)\n",
    "raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(raw['.SPX'])\n",
    "data.columns = ['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "uuid": "11984b1c-5248-4640-8f3b-a85040eb5683"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>2557.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>2560.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>2581.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>2572.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>2575.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close\n",
       "Date               \n",
       "2017-10-25  2557.15\n",
       "2017-10-26  2560.40\n",
       "2017-10-27  2581.07\n",
       "2017-10-30  2572.83\n",
       "2017-10-31  2575.26"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "uuid": "6185abc6-54c5-4711-b273-3252938f3e5e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jDTz/wXbu/NsaKmubufNva3zneLozT6voEhK0hRA9ptpcxgXhZ3LXB22oES7v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\nLtpmZmY18Tc6EE7xQZW9GgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10850a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Close'].plot(figsize=(8, 5), grid=True);\n",
    "# tag: dax\n",
    "# title: Historical DAX index levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "uuid": "9b45b8c2-3b95-4c80-94a0-14f891cdd161"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.99 ms, sys: 857 µs, total: 2.84 ms\n",
      "Wall time: 1.99 ms\n"
     ]
    }
   ],
   "source": [
    "%time data['Return'] = np.log(data['Close'] / data['Close'].shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "uuid": "5fbf45e9-dd56-40ba-8a75-086a80a04d5b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Return</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>2557.15</td>\n",
       "      <td>-0.004674</td>\n",
       "      <td>-0.004674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>2560.40</td>\n",
       "      <td>0.001270</td>\n",
       "      <td>0.001270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>2581.07</td>\n",
       "      <td>0.008041</td>\n",
       "      <td>0.008041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>2572.83</td>\n",
       "      <td>-0.003198</td>\n",
       "      <td>-0.003198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>2575.26</td>\n",
       "      <td>0.000944</td>\n",
       "      <td>0.000944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close    Return    Return\n",
       "Date                                   \n",
       "2017-10-25  2557.15 -0.004674 -0.004674\n",
       "2017-10-26  2560.40  0.001270  0.001270\n",
       "2017-10-27  2581.07  0.008041  0.008041\n",
       "2017-10-30  2572.83 -0.003198 -0.003198\n",
       "2017-10-31  2575.26  0.000944  0.000944"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Close', 'Return', 'Return']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "uuid": "8995980e-7fa1-482e-8996-3a0cc1050359"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bOHpUe2XeeCOS1atNzJsXwerVJoYMCR5KvnGjgSpV4pg2LTJ326uvZnH33d4R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\niqIoSoj5fxSvo26dvWgdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085adeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Close', 'Return']].plot(subplots=True, style='b',\n",
    "                              figsize=(8, 5), grid=True);\n",
    "# tag: dax_returns\n",
    "# title: The S&P 500 index and daily log returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "uuid": "956890ca-7927-4fac-a99a-af7a15cac58f"
   },
   "outputs": [],
   "source": [
    "data['42d'] = data['Close'].rolling(window=42).mean()\n",
    "data['252d'] = data['Close'].rolling(window=252).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "uuid": "f5440e3f-b808-4685-9bec-5f6ca39609c5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>42d</th>\n",
       "      <th>252d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>2557.15</td>\n",
       "      <td>2515.261429</td>\n",
       "      <td>2372.400873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>2560.40</td>\n",
       "      <td>2518.027143</td>\n",
       "      <td>2374.071389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>2581.07</td>\n",
       "      <td>2521.235952</td>\n",
       "      <td>2375.849286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>2572.83</td>\n",
       "      <td>2523.979762</td>\n",
       "      <td>2377.620794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>2575.26</td>\n",
       "      <td>2526.446667</td>\n",
       "      <td>2379.402976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close          42d         252d\n",
       "Date                                         \n",
       "2017-10-25  2557.15  2515.261429  2372.400873\n",
       "2017-10-26  2560.40  2518.027143  2374.071389\n",
       "2017-10-27  2581.07  2521.235952  2375.849286\n",
       "2017-10-30  2572.83  2523.979762  2377.620794\n",
       "2017-10-31  2575.26  2526.446667  2379.402976"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Close', '42d', '252d']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "uuid": "281a5820-2a77-46b4-b399-8c0913423bc3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1085c5940>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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76215DIkLYs2OPG6/eCRBfha+232s3YANGobQAt46tAtzmgamRmj04WBZMqAB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1084cbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Close', '42d', '252d']].plot(figsize=(8, 5), grid=True)\n",
    "# tag: dax_trends\n",
    "# title: The S&P index and moving averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "uuid": "1b12ae02-5e35-47ff-a9c9-8563e468b489"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "data['Mov_Vol'] = data['Return'].rolling(window=252).std() * math.sqrt(252)\n",
    "  # moving annual volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "uuid": "2e75f8fd-bcf8-4c36-93b9-974ef94366c7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BXAjhPzJFqwh6u3ebfII4wNSpocTFRfHyy0YAfvrpgs9XumCBhbp1o7KCeHaz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\nOdNBlSo6oZe0pE+bln9zeeb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5nGKVpmmdMv51zeVxUUIUBTp2dONyKQweHJ7vB7EsJrtl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\nb9pkoWvXEv7xj9gDzZIlNq65pjDkOabchY27EBA35lbJzz7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o3t3NgAHpWzbt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103feae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Close', 'Mov_Vol', 'Return']].plot(subplots=True, style='b',\n",
    "                                         figsize=(8, 7), grid=True);\n",
    "# tag: dax_mov_std\n",
    "# title: The S&P index and moving, annualized volatility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "uuid": "85bf9df2-d445-4600-a02e-37cf2b7dc9ff"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "uuid": "0bc55f0d-cd99-45b9-955e-3a126360e94f"
   },
   "outputs": [],
   "source": [
    "# data from Thomson Reuters Eikon API\n",
    "raw = pd.read_csv('source/tr_eikon_eod_data.csv',\n",
    "                 index_col=0, parse_dates=True)\n",
    "spx = pd.DataFrame(raw['.SPX'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "uuid": "73526ac3-4bf0-4455-89b2-f6aa614ffdca"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>.SPX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>2557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>2560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>2581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>2573.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>2575.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              .SPX\n",
       "Date              \n",
       "2017-10-25  2557.0\n",
       "2017-10-26  2560.0\n",
       "2017-10-27  2581.0\n",
       "2017-10-30  2573.0\n",
       "2017-10-31  2575.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(spx.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "uuid": "3a1920c2-8c61-4720-941e-afdb983350aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1972 entries, 2010-01-04 to 2017-10-31\n",
      "Data columns (total 1 columns):\n",
      ".VIX    1972 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 30.8 KB\n"
     ]
    }
   ],
   "source": [
    "vix = pd.DataFrame(raw['.VIX'])\n",
    "vix.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "uuid": "3a437278-4466-41bf-b7f2-f9c17d2c44a7"
   },
   "outputs": [],
   "source": [
    "data = spx.join(vix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "uuid": "fc5fc92a-3475-4e4b-a5fe-145809d35919"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>2557.15</td>\n",
       "      <td>11.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>2560.40</td>\n",
       "      <td>11.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>2581.07</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>2572.83</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>2575.26</td>\n",
       "      <td>10.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               .SPX   .VIX\n",
       "Date                      \n",
       "2017-10-25  2557.15  11.23\n",
       "2017-10-26  2560.40  11.30\n",
       "2017-10-27  2581.07   9.80\n",
       "2017-10-30  2572.83  10.50\n",
       "2017-10-31  2575.26  10.18"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "uuid": "07158c72-907f-4636-ad40-95182b7728e3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vAv/WNG2lUmoIMAo9iAuR8IYNS+WDD1J8tnfubPcJ2IDfMdlCCFFr73FN0z4B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1089cf978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(subplots=True, grid=True, style='b', figsize=(8, 6));\n",
    "# tag: spx_vix\n",
    "# title: The S&P 500 Index and the VIX volatility index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "uuid": "17ae59ff-9863-4c30-9493-22d44c0e5edf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.003111</td>\n",
       "      <td>-0.035038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.000545</td>\n",
       "      <td>-0.009868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.003993</td>\n",
       "      <td>-0.005233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.002878</td>\n",
       "      <td>-0.050024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                .SPX      .VIX\n",
       "Date                          \n",
       "2010-01-04       NaN       NaN\n",
       "2010-01-05  0.003111 -0.035038\n",
       "2010-01-06  0.000545 -0.009868\n",
       "2010-01-07  0.003993 -0.005233\n",
       "2010-01-08  0.002878 -0.050024"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = np.log(data / data.shift(1)) \n",
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "rets.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "uuid": "771c53bf-78fb-4260-865b-39307973cb77"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qlb7dFNWsIQAwcWIK/vUvfcK6Xr0sNGsWKKjNsFY/UbjnnjS/I2ilfP+9TzAv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\nImOCObntNhdeecVfGsycWYo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/fTGOHaOMIwgg8Uz0V11VhauuIt+jeEMCnjAlF17oxoUX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\n+Dgk7qL3J1isCtNLiVld7V7K5d13/XelVUj7qd8hh3SO2k+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AffZxfq+QFUEi9O2bfQ3v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\n5P8BZc32dNaHI1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1057ec128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets.plot(subplots=True, grid=True, style='b', figsize=(8, 6));\n",
    "# tag: es50_vs_rets\n",
    "# title: Log returns of S&P500 and VIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "uuid": "709bc1e8-03a8-47c6-9b21-4efa08052dab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -6.45336250e+00,   2.34474296e-03])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdat = rets['.SPX'].values\n",
    "ydat = rets['.VIX'].values\n",
    "reg = np.polyfit(x=xdat, y=ydat, deg=1)\n",
    "reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "uuid": "24c708df-1e81-48c6-b1c2-890dd52e541f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'VIX returns')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4imN5gMnEU8SrXMHB/Mwczo7YLRs7tns0qJyxcVaBoZcjFDFBIRKE4yh88hFa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\njDHGVOJEYYwxphInCmOMMZV8B66S69NI9V/5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111343d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xdat, ydat, 'r.')\n",
    "ax = plt.axis()  # grab axis values\n",
    "x = np.linspace(ax[0], ax[1] + 0.01)\n",
    "plt.plot(x, np.polyval(reg, x), 'b', lw=2)\n",
    "plt.grid(True)\n",
    "plt.axis('tight')\n",
    "plt.xlabel('S&P 500 returns')\n",
    "plt.ylabel('VIX returns')\n",
    "# tag: scatter_rets\n",
    "# title: Scatter plot of log returns and regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "uuid": "e1f9009e-5b73-4e04-9b10-deea36f4e508"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>.SPX</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.808372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>.VIX</th>\n",
       "      <td>-0.808372</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          .SPX      .VIX\n",
       ".SPX  1.000000 -0.808372\n",
       ".VIX -0.808372  1.000000"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "uuid": "a534d3db-df59-4a31-b0b7-1ab3c19d77d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111333898>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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75eXd190U19Vv9Z+eBnwNDAIma623RNjvT0ADRikVAL/FtAGbBXxpje8DXK61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\nkNBBeRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11145c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets['.SPX'].rolling(window=252).corr(rets['.VIX']).plot(grid=True, style='b')\n",
    "# tag: roll_corr\n",
    "# title: Rolling correlation between S&P 500 and VIX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## High Frequency Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "uuid": "cd3bd5f2-565a-4158-a796-e8ec41f76d88"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "uuid": "946e1882-04bb-4809-869f-1e7dd8ad396a"
   },
   "outputs": [],
   "source": [
    "# data from FXCM Forex Capital Markets Ltd.\n",
    "eur_usd = pd.read_csv('source/fxcm_eur_usd_tick_data.csv',\n",
    "                     index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "uuid": "1fce3fb2-c664-4f77-80dc-0e4907f86dac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 17352 entries, 2017-11-10 12:00:00.007000 to 2017-11-10 14:00:00.131000\n",
      "Data columns (total 2 columns):\n",
      "Bid    17352 non-null float64\n",
      "Ask    17352 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 406.7 KB\n"
     ]
    }
   ],
   "source": [
    "eur_usd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "eur_usd['Mid'] = eur_usd.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "uuid": "b0fa9c58-2087-4f99-a7f1-efeb490bb456"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mAfgdgDLG2Dyvv9j1AB6RtLkLwGgAFzHGrgEAzrnT2+4Jrz/ZFADzvLs4GWPP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\ndNvODu79yxr+8FTwzXPv3s60A2hXrunuLUvUMBusrq7uXsV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bmYmJAOr+FRERkcGo7X8X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\nLJWGpaGVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111388860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eur_usd['Mid'].plot(figsize=(10, 6));\n",
    "# tag: eur_uusd\n",
    "# title: EUR/USD tick data for two hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "uuid": "47a27b89-f6a1-4b3c-9944-8a6109642c72"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bid</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Mid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-10 12:00:00</th>\n",
       "      <td>1.16406</td>\n",
       "      <td>1.16407</td>\n",
       "      <td>1.164065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10 12:01:00</th>\n",
       "      <td>1.16396</td>\n",
       "      <td>1.16397</td>\n",
       "      <td>1.163965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10 12:02:00</th>\n",
       "      <td>1.16416</td>\n",
       "      <td>1.16418</td>\n",
       "      <td>1.164170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10 12:03:00</th>\n",
       "      <td>1.16417</td>\n",
       "      <td>1.16417</td>\n",
       "      <td>1.164170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10 12:04:00</th>\n",
       "      <td>1.16425</td>\n",
       "      <td>1.16427</td>\n",
       "      <td>1.164260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Bid      Ask       Mid\n",
       "2017-11-10 12:00:00  1.16406  1.16407  1.164065\n",
       "2017-11-10 12:01:00  1.16396  1.16397  1.163965\n",
       "2017-11-10 12:02:00  1.16416  1.16418  1.164170\n",
       "2017-11-10 12:03:00  1.16417  1.16417  1.164170\n",
       "2017-11-10 12:04:00  1.16425  1.16427  1.164260"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eur_usd_resam = eur_usd.resample(rule='1min', label='last').last()\n",
    "eur_usd_resam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "uuid": "7730056f-4ce8-4a23-853e-f5206eb86ea7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1112ea3c8>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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et5PugdnCwTAMjrQNUVbslkqrOcDldFLvL6GjfwzDMNL24Qyn4W+w2ba+Em+R\ni8Or2O/w9JEeXE4Hbzpvw5LcLy3hoLV+BHhkxuE7Zoy5Hbg9xdzHMc1Jqa57HFNYXD/j1PvTWZcg\npGIqAW7uh4rT4aChupTuoXHihoEz6YHVPTjOYCDMgV31OJ3ijM4FG+vL6X6tl8FAOO3or8E0IpVs\nitxO1KYqXjoxwOFTgwlNYjURDEUoLymionR2Ymc+kCQ4YdUxXxhrMo3VpUxG4ok3VJtXLNPEni2r\n7wFTKDZYJUhO96bfoCedSKVk3nr+JtwuB9+952WOt49kvshlzvhEdEmd7SIchFXHcDC9h0pjtel3\n6Jrhd7Dt1qvx7bNQbEwIh/Qdp7ZwmCsBbiZqk5+Pv2MvkWicv/3xoQXLo6wkDMMgFI5SWizCQRCy\nZiiR4zC/+t1oO6WT/A7RWBzdNkxTTakkv+UQu3jh6Qy6t9nZ0XOVzkjF2Tvr+Ojv7SYUjnHv46sn\nPSoSjROLG6I5rDUOtwwuWCFUSJ/hUat0RhpmJZgesXSiY4RwJCZaQ47xV3gp9bqzNCtlJqTP391A\nscdF/0goo3nLGbsulQiHNUTXwBi3/fAQP3n0ZKGXsmqwzUqV6QqHgam3WfE35AeHw8HG+nJ6B8cJ\nWw2XFmJoNIzL6aCitCjj+1X7ihlcRN/q5ca4JRxKrTa3S4EIhwJzpHUIgKHR7DtlCXCsfTjxVjoc\nDFNeUkSRe/7tXeJ1U1numaY5HG4ZxOV0oDZVzTNTyIYN9eUYQEd/eqYluwOcM4vyJdUVXsbDUSYm\nM68EuxwJhU2BKprDGuI1SziMhnJfVXGtYBgG3/7xS3zzBy8wPhFlaDS8YKSSTVN1KQOBMOFIjNHx\nSVq7R9mxoZJiz+otwVAoMnFKx+JxhoOz24OmS7Xlp1gt2oOYldYYccPgtbZhAEbHRThky8RkjPFw\nlGAowk8fO8nEZCzth0qjleTWMzjOEy93Y8CqLb9QaDZmEM46EpzEMNIPY51JteWnWC2+PBEOa4yO\nvjGClsYQDEXy0rBjLRAYn6qs+tvn2oGFI5VsEuGsA+M8+Hw7HreTi/c15X6RAutry3A4oD0N4WC/\n8Vdn6Iy2sYVKtu1JlxtTPgcRDmsC26TkwAxVC0fSc9QJ07HLZdRWFmOL17Q1B0s4PHDwNP0jE1yw\np4HykswdoMLCeIpcNFaXcrpvbMEXoYde6ABgc1N21UbtHgeDojlkjQiHAmI7o3duNJ2fQTEtZUVg\nzBQOV5yzgfW1ppkoXZ+DnetwstNsGfrGczfmYYWCzYa6ckLhKAPzFOFr7R7lqcPdbKwv57xd9Vnd\nZ8rnsDo0BxEOa4h43ECfHqaTAAr5AAAgAElEQVS2sphmq22lOKWzwzYr+Su8vP+qnayrLWNXs3+B\nWSa1vuJEw5SdG6sSdnEhPyT8Dn1zm5bufvg4BvCuy7dnFakEU+ao1aI5iFlpDdHWO0ooHOWMZn8i\njluc0tlhm5V8ZR7UJj83/9n5CXPRQjidDhr8JQC88dylqXa5lllnaXapquECvNIywOFTQ+zZ7F9U\nronX46Ks2L0KHdJLl+cg8XoF4rVWM0ppV7OfSNRshhIMpW5ZKcyPrTlUlmVXrfKifY0caR3i7B0S\npZRv5qpnZfPTx1pwAH982fZF38tf4c24h8RyRfIc1hDHO8yqkWpjVcIBKppDdtg+B1+WwuGt5zfz\nmXedldd+vIJJvb8Ep8ORsslSe2+Qk50B9m2rSZhaF0O1r9gMc55Y+Ylw4nNYQ3T0j1FW7MZf4U2Y\nlYLic8iKkbFJXE4HZUtYsVLIDrfLSW1VcUqz0uMvdwHw+jNzE0psV3MdXAXVB8bDUTxFziV9gRHh\nUAAi0Ri9Q+Osqy3D4XAkaQ5iVsqGwNgkvjJP2h3GhMLSVF1KMBSZ9jIUjcX53SvdlJcU5SwJ0W+H\ns66CLOnQEvdyABEOBaF7MIRhTDnn7M5OYlbKHMMwEsJBWBmkKpX+4vF+gqEIF+1tzNnbsa05rIa6\nZePh6JJGKoEIh5wxEgynneHc0W+G8dnCobTYjcMhoazZMDEZYzIaz9oZLSw9U07pqQJ8j71kmpQu\nyZFJCZLMSitcc7Ab/Sy15pDW3ZRSjcDNwH6t9YE5xlwJfAu4U2v93aTjTcB1wChwEXCX1vrepPM3\nAZ/WWtcmHbsAeBMQBy4HPqS1Pp3hZ1syXjoxwP/58Yt8/J17OZBG0k5nv/nGZCdsOS3TkiTBZU7C\nGb1EfXWFxTOzj8bQaJiXTw6wpamCDXW5yzOZypJe2ZpDIRr9QPqhrJcA9wJnpTqplPIBVcChFKfv\nAD6otR5VSt0JVCbNuwyYlq1kXeuzWus/sn7+ATCY5joLwoPPm/V8jpwaTCkcJiPm263tW+i0Shbb\nmgNAeUmRmJWyYMQSDpVp1lISCo9d7NA2Kz17pAfDIOc1rfyrRHMoRKQSpGlW0lrfjfnmP9f5gDVm\nGpbG0Qxco5S6Hvgo0GWdawDeA3xnxrS3AUGl1GeUUl8CztFap99bcImx33oAWntSZ33eed+rfOF7\nTya+5E4rUinZFFJR6mEsFCEel+J7mSCaw8rDV1pEided0ByeP9qHAzhXZVcqYy48RS7KS4pWfJZ0\nIRr9QP6T4JqB/cD9WusWpdTNwI1Kqa8AXwOuJ0mTSJpzPvBnQAx4SCk1oLV+aKGb1dVlHxud7dyH\nX+rCdjV09AWpri7DleRQGwmGeeFYP7G4QWvfOBfsa6R3OITa5Ke+3pcYV1NVwtHTwxSXeRfsYCZM\nEdd9AGxo8i3q+883y3lthWBTQwUnOoZxetwc6xjhjC3VbN9ck/P71FeX0tEXpLa2fEmi2SLROOMT\nkZz+DQ+FTOFQXVU65z7Kx/7Kt3AIAH1aa7vT9+PADcB9QATTF+EHSpRSNwD3WHNe0FpHAJRSTwKX\nAgsKh76+hZuIpKKuriKruYZh8KsnT1HkdnLm1hqeO9rHy7qH9Ul204de6CBmaQMPP9dGucdJPG5Q\nX1U87Z5el7lxW04PJXwRwsJ09Ji/Q0csnvX3n2+y3V+rmRqfF91m8J+/eg3DgDO3VOfld+QrKeLk\nZIxTp4eWpNru3Q+f4LfPtfPNT1yUs/t1dptFIYmn3uOL2V/zCZWso5WUUmVKqboFhh0DxpRStnbQ\nDBzVWh/UWn9Ma30r8HdASGt9q9b6GKYQ2Jx0jWbgaLbrzCfHO0boGQpxrqpLtJVsm2FaevrVHhyY\n2bsvnhig1XqYrZshAMrtRDjJdcgIu3SGhLKuLGyntF2a+5ydCz1KssNvVWd97MVOorF4Xu6RjD49\nRDgSm7fqbKYsa5+DUupS4ANAk1LqJqVUCXAt8NWkMZ8CzgTerJR6N4DWOmqN+4ZS6kbgYuCmpDnb\ngT/H1BxuUkqVaa1fA76vlPqGUuoWTB/Ffy76k+YBO/zu9fua2NRgSmD74Q9mueBjp4fZsbGKi/Y0\nEp6M8ZuDpvN6lnAoMR9ukiWdGclF94SVgy0cwpMxNjWUU1tVkpf7nH9GAx63kx8/fIKb/vHpRA+V\nfGAYRiIScTSHddIKUZEV0jQraa0fAR6ZcfiOGWNuB25PMfdxTHNSqusex/Q7XD/j+P9NZ12F5nDL\nIJVlHlSzn/BkDAfQliQcnjnSiwGcv7uBjXXl/PKZtoTwmGk6ksqs2REYl9IZKxE7EQ7ypzWAWYb9\n1o9dyM9/18rDhzr4p5+/yl9//KKsS4HPx3BwMvGWn8uw9GWtOQiziURjDI2GaaopxelwUOJ1U+8v\noa0nmEiGe/pIDy6ng/NUHVvX+xLRSaVe96ykrQq7hIZoDhkhpTNWJg3+Euxv7Nw8CgcwGz+976qd\nXLC7gYFAmBNW0ctcY4eoQ24tAKECaQ4iHLLELgVcWzmlDm9qqGDc6nLV3hektXuUPVuqqSj14HQ4\nONv6I1hXVzbrYTZVQkN8DuliGAYjUjpjRVLkdrFlnY/NjRWzTKz54vzdDYDpB8wHHXkSDuOiOaws\nbIdTbdVUA/RNDWaUUmvPKP/2a9OHftlZ6xPn7TekVFmgdmTDfJvqOd3LsfbhRa589TAxGSMipTNW\nLJ9979l8/k/PWTKt74zNZmOtg6/1Eovn3jnd2T8VjJIPzaFkiU2nIhyypM8SDnVJmkOz5ZS+55GT\nHD09zNk7ajkrqYHM7s1+Pnr1bn7vos2zrle+gM8hEo3z9/ce5j9+cyxXH2HFs9g+DkJh8Ra58HqW\nLrHL5XRy3q56AuORRP/2XGI7oyHXwsFs9CNmpRVC/3AIgJrKZM3BFA7dg+OUeF28/yo1bY7D4eDC\nvY2JtP5kvEUuPEXOOR1ZvUPjxOLGqml7mAsSpTNEOAhpcv4Z+TEtGYZBR/8YtdbzIKdmpQnzWsVL\nKEhB2oRmje1zqEsKwfOVeagq9zAcnOSPL9ueUgjMR0VJ0ZytQrusOjSjY5PE4nFcTpHrUjpDyJTt\nGyrxV3h5/mgfxUVH0aeH2LOlmndfsWNR17UjlXZv9jMaiuQ4Wim25I1+QDSHrOkfCeF2OWYVfHvL\n+c1cetY6Lj1rXcbXLC/xzGlW6hwwnV0GEBiTiCaY0hzErCSki9Ph4PzdDYTCMX77fDvtfWM50SIS\nxTRrysyXvInc+hyW2hkNojlkTd/wBDW+4lnx0lcd2Jj1NStKi2jtiROOxPAWTVchu5IaowwHwxlr\nJasRuxRzlVRkFTLg6gs3U+MrZmN9OT988Dit3aPEDWNRuQ92pNL6ujLKSoro6s9drdDxcDSRB7WU\niOaQBROTUYKhSM6zOqec0rNNS8mbzc4KXut0WQ7AphqpRSWkT2mxmzeeu4GdG6uorvASNwzGFukj\nSC7DX1FSxGTUfMlbLIVq9AMiHLIi4W9IckbnAr9VybF/eHpdlrhhJMobAwyPiVMaoGvALH1eiLcq\nYXXgs7RO20SZLZ39YzgdDhr8pYmw9MUKHChcox8Q4ZAV9sO7JsfCobnRjHZq6QpMOz44MsFkNI7P\negiK5mD+0fQOh2iqnZ1QKAjpYke6LUY4mDWVxmioLqHI7UwIh1yUwilU6QwQ4ZAV/SNmGGtdjs1K\nW9eZ/R1OzhAOnZa/YVez2TRvJCiaQ8/QOIYB65Jq9AhCptjBDIFFvHANBycZD0dZZ5k3ExWWM3RK\nt3aP8sV/eIqugSkTcqEa/YAIh6ywzUq51hxqfMX4SotmaQ72ZrGFw7BoDokWk+JvEBZDLjQH29/Q\nZJUBSVQ7yFBzeOFYH92D4xxumeqKbCfAieawQuizEuCSs6NzgcPhYOu6SgYDYYaTtAM7Umn7+krc\nLicj4nNIhPY2ieYgLILKMtPPt5i/KVs4rJ8pHDL0Odh+xf6kXhCFKroHIhyyYmBkAk+RMy+O0C1N\nlt+hc0p76BoYw+GABn8plWUe0RyYEpiiOQiLIReaQ0euhIO1p5MbBY1ZpqnSYgllXRH0jUxQW1mS\nF0fo1nVm07xkv0PXwDj1Vaazq6rcQ2BskrjduHqN0tU/hsftzLlpT1hb2D6HxQR5dA5YkUpWA6Ns\nzEpxw6B7yNIcAlPCoS9FmZ6lQoRDhoxNRAiFo4kaKrkmoTlYwiEwPkkwFEm8IVeVe4nFjWXZMW5s\nIsKPHjqeqAWTL+zQ3sbq0rw0bRHWDkVuJ2XF7kS72UwxDIPOvjHq/ebLG0yV38/EIT08GmYyYlaK\nTdYceoZM4dDgz0+nvPkQ4ZAhdhhrrv0NNqXFRTRWl9LSFTAfggnziflWYpfrGF6GBfieebWHXz7d\nxhMvd+f1PnZob6P4G4Qc4CvzZK05jIyZkUrJnR3LS0z/QCb94LsGp1d0nZg0fQ29QyEcTO8bs1SI\ncMgQO4w1n2reliYfoXCMnsFxTnWbbUVtzaGy3HagLT+/w5D1B5bcKjUf2KG968TfIOSAyjIPwVCE\naCzzHg8dMyKVwGxk5C1yEQxF076O/RJoRyXZ2kPv0DjVvuKEVrKULOgCV0o1AjcD+7XWB+YYcyXw\nLeBOrfV3k443AdcBo8BFwF1a63uTzt8EfFprXTvjevXAC8DXk6+3HJjqAJc/4bB1nY8nD3fzwweP\nc7hlEJfTwY4Npi+iyrKRDi/DXIeAFfHR2hNcYOTisEN7m5aog5iwurH9DqPjkYxrls2MVLIpL3HP\nWWE5FbZw2N3s57mjfQwETL/mcHCS3Zv9Ga0pV6Qjji4B7gVSGneVUj6gCjiU4vQdwG1a69uAD2M+\n8O15lwGzPrVSygncAhxMY21LTn+KDnC5xk6Ge+nEAGXFbq5/z1kJZ1dCc1iGEUt2FFXXwBiRaO47\nbdl0zTC1CcJiWEw4a3JNpWTKSzyZaQ6D5nX2bKkGzOdMr+WMrvcXZp8vqDlore+2HuRznQ8Adyul\nrk4+bmkczcA1SinbYPZt61wD8B7gVuCDMy75eeBO4ONpfoYEdXUVmU7JeG5wwvzC1da6vJWKrvKX\nUVtVQl1VCZ+/5jxqkuyNWybNh+5kzFjU580HdjZnLG4wHjXY3pTZ+l463scTL3by4rF+wpEYX//E\nxTSmMB31ByZwOmDvznqK3EufOZoNy+27EqZYZzXpwuXK+HvqGzH34j41fS9WVxbT2jOKr6p0VoXl\nVPSOTFDtK2bvjnr4lSYUiTMRMyMSt26oWnBd+dhf+cysaAb2A/drrVuUUjcDNyqlvgJ8DbgeqEye\noJS6HBjXWj+tlMpYOPT1ZWfrrqurSHtuR28Qr8fFxNgE4fH8mXa+9tHzcTkdxCej09YWj5gP4K7+\nYNafN1/Y3fEAXtQ9VBan/+A+0THCLd9/DgBPkZPJSJyb/+lpvviBc6b90cUNg7buUWqrShgeGp/r\ncsuKTPaXsPS4MR/CbZ0jbK5L31RpGAatXQHq/KWz9qLH8hGcahuk2je/lSEcidE3FGLXpipchvny\n19YVwGmFq5cVOefdP4vZX/MJlXx6OQJAn9a6xfr5ceAy4BwggumL+DhQopS6QSm1A3iH/TOwD3iT\nUupDeVxjxgwEJqj1Fee92Jvb5Ux5j4rSIpwOx7IzK8UNg8BYJNETuDVDp/QDB08D8NHf2813P/0G\nXn9mE609o/xgRs/snz/ZSjAUYcf6ylSXEYSMyTYRLjA2ydhEdJa/ATJLhOuxIpUaa8qoLPfgcjro\nH5mgxxI49dXL1KyUCqVUGVCqte6bZ9gxYEwpVam1HsHUJI5qrQ9i+ROUUpuBj2itb7XmfDrpHruA\ng1rr/5fNGvPBuJXjULOhcA8mp8PsPmc7pH/7XDveIheXnNlUsDWBmfATNwzUxipeOTmYUcTS0GiY\n53Qf6+vKuGB3Aw6Hg/e9aSenukd5+FAnfl8xb3ndJo63D/PTx05S7fPyriu25/HTCGuJbIvvdST8\nDbMf3pkIB7tshp23U1NZzEBgAm+REwdQn0f/5nwsqDkopS4FPgA0KaVusvwH1wJfTRrzKeBM4M1K\nqXcDaK2j1rhvKKVuBC4Gbkqasx34c0xN4SZL4NjnPpx0vbcu9kPminwV3MuUyjIPI2OTvHpqkH9/\n4Cg/+O3RgmdM229dtZXFrKst5XRvkHg8vTU9cqiDWNzgjeduSGhLniIXn/iDvZSXFPGTR0/yxX94\nkr//2WGcDgcff8feRKKRICyWhOaQYSLcXM5oyF44gFmAMzA2SXvfGH6ft2B+tXQc0o8Aj8w4fMeM\nMbcDt6eY+zimOSnVdY9j+h2uT3Hun4F/XmhtS83AEoSxpkNVuZdT3aP808+PACRyIgpZZ8guI15Z\n5mFTQwXtfWP0DC28pmgszsOHOin1urlwd+O0cw3+Um756Pnc/1Qrv32ug2gsznuv3ME2MSkJOaSi\n1IPDAYEMw8OT+0bPvmYWwsGKvrOfL8FQhF11VRmtKZdID+kMmMpxWPpsxWTsLOmh0TA1Pi8DgTAn\nOwOFFQ6W5lBZ7sXrcfO7V7pp7RldcE0HX+slMDbJVQc2JvwVyVSUenj3FTu46sAmOvqD7NlcnZf1\nC2sXp9NBRaknY59DZ79ZEDNVSHWZpTkMjYb5f/cf4ZWWQb74/nNTWh26BsZxu5zUWo7r5DGFCmMF\nyZDOiAGrIFbNAtEH+cZWg2sri/nI23cDsxsE2YxNRHhO9/Hvvz7KTx49iZEn81NCOJR5aG4oB6Bt\ngWS48YkI//XoSRzAFeesn3esv8LL3i010vVNyAu2qTYTOgfGqassSWn2qbCEw/1PtfLYS10MjYb5\n3Stds8a1do/S3htkXU0pTqe5t5MtEw3VhXsRFc0hA5YiOzoddmysoqzYzbVv3cW29ZW4XY5pJb5t\nTvcG+eq/HpxWFmDfthq258EsYzvIK8s91FeZbzvH20cSzdEj0RjHOwLTsr3vvO8I/SMTXH1Rc0Hf\nkAShsszD6d4g4clYSg12JqNWQcxtVsLqTGyfg2HAxfsaefrVXp56tYerL9qceMEZm4hwx09eJhY3\n+MNLtyXmJlsm7L+lQiDCIQMGRibwuPPTxyET9myu5va/fH1ik22sr6CtZ5RINDbtLeaZIz1EY3Eu\nO3s962vL+PcHjvLgc+15EQ6BhObgpbTYTWN1Kcc7RvjUtx+jqaaMnqHxRNb09g2VbKov59Dxfs5o\n9vPOS7bmfD2CkAnJTul6z8Jv64ks/TlKuPgrvLz5dRvZWF/ORXubmAjHeO5oH6d7g2xqqMAwDP4p\n8XK0mTO31STmJlsmClGN1UbMShnQPxKipjL/OQ7pkLyGret8xOLGLDPOSycGcLucvOvybVxxjikg\nnn2tNy89qIeDkzgAX5kpOD/5h/t4+4XNNDdW0DUwRoO/lKsObOTsHbUcbx/hwec7qCr3cN3v70mo\n04JQKHzlmYWzdi3QidDhcPDuK3Zw0V4zxPz83Q0APH2kB4BfPtOW9HK0Zdrcqgoz1wGgroDCQTSH\nNAmFo4xNRNkyhxpZSLY2+fgtpt/BjuQZDExwujfI3i3VFHvMr/mKczfw/V9pHj7UyTtmbMjFMjI2\nSUVpES6n+b6xvraMP7JUZcMwpgmzE50jPHqok8vPWZ+3EiSCkAmZ1lfKtBPhmdtqKPa4eObVXs7c\nWsM9D5+kstzD/0jxcuRyOmmqKSUSM9IqvZEvRHNIE9sZXVtgZ3QqbIHVkuSUfunEAMA0dfXCPQ2U\neN08/ELHnOWJg6EIR1qHMl7DSDCMryx1RcuZmta2dZV86G1nsLlx+QlaYW1iV2MdCGQmHNalWfzR\nU+Ti7B11DAQm+D93vwTAx9+xN2HOmsmn/uhM/ue79qd17XwhwiFNlksCXCoa/CWUet2c7EwhHLZP\nVUMv9ri5ZF8TI2OTHNS9Ka/1b7/WfPMHLyRir9MhPBljYjJGVbloAcLKpL7KNN/0DYUWGGnSNTBG\nZZkno97OtmkpPBnjjy/bxs6Nc+cw1FaVJNZUKEQ4pMnAMhYODoeDLet89A6FCIYiRKIxXm0dpKmm\ndNYGs0NGf/tc+6zrhMJRXjjWD2TWsMdWxed6CxKE5U69ZdvvSaOYYzgSY2BkIuOS8bs3+9lUX84F\nexp48+s2ZrXOpUSEQ5oMLJMEuLnY0mSaaB5/qYsjrUNMRuLs31Y7a1xDdSn7ttZwoiPAqe7p4a+H\njvUnIora+xZu2GOXx0hOgBOElUiJ142vtIjeNDSH7oFxDDJvNuV2Ofnyh1/H//i9PcsiqGUhRDik\nSX9geeQ4zMW5O+twuxz86KHj3PGTVwDYv70m5dg3nrsBmK092JEUAB19Y/Pe76ePneQvvv0YJzsD\niQqxojkIK5n66lL6RyYWbBfaZTXmaSpQtdSlQoRDmgyMhHC7HMs2uqa5sYJbPnoBF+9tJBqL4yst\nmrMG0d6t1TT4S3j61V4CVrGxYCjC4ZZBmhsqqCgtmldz+PmTp/jZE6cIhaP84unWJM1hef5uBCEd\nGqpKiBtGIvhkLrr6589xWC2IcEgDwzDoGzY7NTmXsTpYV1XCR67ezdevu5AbrzkPtyv11+t0OLji\nnA1EY3Eee7ETMGscxeIG5+9uYH1tGX3DE4QnY7Pm/vrZ09zzyElqfF6aakp54Wh/IkpKNAdhJZPw\nOwzOb1qycxxSFdxbTYhwSIOh0TDBUISNdeWFXkpa1FstRufj4n1NeItcPPDsaX7xVCuPvWQKiQO7\n6tlgfU67Xj1AJBrnrl9p/vO3x6gs93D9e8/mLa/bRNwweOqwaY4Sn4OwkrFLuPQu4JTuGhin2ONa\n9dF5IhzSwA4RXY4JcNlSWuzm6ouaCYxH+PHDJ2jpGmXHhkpqKotZb7VK7LBMSyNjk3zjP57n4Rc6\n2FBXxhfedw4N/lLO391AeUlRopeEaA7CSsYucjefUzoWj1ul6EtXhFN5MUiGdBrYZhM7Imi18PYL\nN3PJviaOtA5xojPAhXvMfgq25tBuOaXveeQEJzoDXLCngQ++ZVcia9NT5OL1+5v4xVNteIqcFKdR\nsEwQlit22Hfv8NzCoXswRDRmFLQ8/lIhwiENTnYGcACbG+duxr1SqSz3csGeRi7YM9Vox+5s1dEf\nJBSO8uyRXmori/mzq3fP8rlcfvZ6fvl0G1Xl3lX/JiWsbkqLiygvKUr0dJ5JLB7n336lATij2b+U\nSysIIhwWIB43ONU9yrraMkq8a+PXVeJ1U1tZTHvfGAdf6yUcifHWfZtSOuNrK0v48NvOoCyDTFFB\nWK40+Es41T1KLB5P1Amz+cmjLejTw5y7s46L9jbOcYXVQ1pPO6VUI3AzsF9rfWCOMVcC3wLu1Fp/\nN+l4E3AdMApcBNyltb436fxNwKe11rXWzweATwMvAAp4Rmv9j1l8tpzQOTBGOBJbdSalhVhfW8aL\nJwb41bOncQAX7Zv7j+HifU1LtzBByCP1/lJOdAYYCISnVRc4dLyf+59qpd5fwofedsaa0JLTdUhf\nAtwLpPyNKKV8QBVwKMXpO4DbtNa3AR/GfOjb8y4DZupnTcC3tdbfAj4BfEMpNTvVd4mwndFbV5Ez\nOh3WW36Hzv4xdm/2L9vMcEHIJXb/hJkRSz9/8hROh4NPvHMvpcVrw4KQlnDQWt+N+eY/1/mANWYa\nlsbRDFyjlLoe+CjQZZ1rAN4DfGfGtX6mtX4m6VAUWLhLd55Yrc7ohdhQN+Vwu+TMdQVciSAsHXPl\nOvQMhqj3l7CpYfX5Heci3yKwGdgP3K+1blFK3QzcqJT6CvA14HpgvrZkfw58TWs9ks7N6uqy/+Lm\nmnu6dwyP28lZuxvnTCpbjezdGQdepaykiKsu2oKngHXlVwOL2ZvC0qG2RgEYDUcT39lYKEIwFGHX\n5upl+z3mY135Fg4BoE9r3WL9/DhwA3AfpjZwHaZZqUQpdQNwj9b6GIBS6k+BMq31zenerK8v/Uqi\nydTVVaScG47EONUVYOt6H0OD89caWm0UO2HXpirO3FbLyHD65buF2cy1v4Tlh8dh5uy0dowkvrPW\nbvPfytKiZfk9LmZ/zSdUshYOSqkyoFRr3TfPsGPAmFKq0nr7bwaOaq0PAget62wGPqK1vjXp2n8G\nlGutb1ZK7QPCWuuj2a41U0LhKMPBMKd7g8QNg61rzKQEZgXJz/3pOYVehiAsKWXFRZQVu+lJSoSz\n8x4K3V9hqUk3WulS4ANAkxVddBtwLbAP+Jg15lPAmUCNUqpPa/1DrXVUKXUtplO5DTP66Pqk6263\n5pdY1/1b4Err+i8opd4J1AB/ASyJcIjHDb74j08lKo3C2vM3CMJapqmmjJauAJFonCK3M+GcLmQ/\n50KQlnDQWj8CPDLj8B0zxtwO3J5i7uOY5qRU1z2OKSyuTzp8L/P7IfJKMBRhJDhJfVUJuzf7KSl2\nc/aOggVLCYKwxGyoL+d4xwid/WM0N1bQJ5qDAFONa/Zureb9V6kCr0YQhKVmY71dPiZIc2NFotbS\ncu3lki/WTvhNmkjLS0FY29jVl0/3moUn+4ZD+Cu8ay5iT4TDDBJdzaT8tCCsSeyqxKd7g0SicQYD\n4QVL4K9GRDjMIGCZlZZrxzdBEPJLiddNXVUxp3uD9I+EMFh7/gYQ4TCLRMtLEQ6CsGbZWF9BMBTh\nWLuZf7vWIpVAhMMsAiIcBGHNYzulnz9qpnGJ5iAkNAcxKwnC2sVuePXqqUFgqubSWkKEwwxGxiYp\nK3avqTpKgiBMZ2ODKRyiMbOchjikBUaCYYlUEoQ1Tm1lMV6r7W2p1015ydprZiXCIYlINM7YRFT8\nDYKwxnE6HIl8h7XojAYRDtMYHRdntCAIJrZTei06o0GEA0OjYcKRGCDOaEEQpthgCwfRHNYe0Vic\nL3zvSf7xpy8DydnRIhwEYa1z9o5adm2q4sCu+kIvpSCsaeHgcjpwu5y8cqIfkLpKgiBMUVXu5XN/\nes6aag2azJoWDg6HgzzHwpgAAAdjSURBVE0N5XT2jzExGU1KgJNoJUEQ1jZrWjiAmSZvGGaRLSmd\nIQiCYLLmhcMmK9mlrScoDmlBEASLNS8cmi17YmvPKCNjkzgdjjWZ8CIIgpDMmu8E11hTSpHbyeme\nIKFwlIqyIpxOR6GXJQiCUFDSEg5KqUbgZmC/1vrAHGOuBL4F3Km1/m7S8SbgOmAUuAi4S2t9b9L5\nm4BPa61rk469HzgbiAEntNbfy/SDpYvb5aS5ycepzhGcDgeNNaX5upUgCMKKIV2z0iXAvUDKV2ql\nlA+oAg6lOH0HcJvW+jbgw8ALSfMuA/wzrrUBuB64Xmv9OeDPlFI70lxnVmxbX0k0ZjAZjUukkiAI\nAmlqDlrru60H+VznA8DdSqmrk49bGkczcI1Syk4z/LZ1rgF4D3Ar8MGkaW8GntNaG9bPTwJvBY4t\ntM66uuzikbeur0z8v6GmLOvrCMJcyJ4S8kk+9le+fQ7NwH7gfq11i1LqZuBGpdRXgK9hagiVM+bU\nY5qgbALWsQXp6xtdeFAKkoWD1+3I+jqCkIq6ugrZU0LeWMz+mk+o5Fs4BIA+rXWL9fPjwA3AfUAE\n0xfhB0qUUjcA9wC9wPaka/iA4/lc5OYmHw4HGIaEsQqCIMAiQlmVUmVKqboFhh0DxpRS9qt5M3BU\na31Qa/0xrfWtwN8BIa31rVrrY8CvgHOVUrZ/40LgF9muMx2KPW4aq01HtCTACYIgpCkclFKXAh8A\nmpRSN1n+g2uBryaN+RRwJvBmpdS7AbTWUWvcN5RSNwIXAzclzdkO/Dmm5nCTUqpMa92OGfX0t0qp\n2zCjnxb0NywWO99BhIMgCAI4DMNYeNTKwFiM3e3JQ+08+Fw7H3rbLorcrhwvTVjLiM9ByCeL9DnM\nmdS15pPgbLavr2T7+pm+cUEQhLXJmi+fIQiCIMxGhIMgCIIwCxEOgiAIwixEOAiCIAizEOEgCIIg\nzEKEgyAIgjALEQ6CIAjCLEQ4CIIgCLNYTRnSgiAIQo4QzUEQBEGYhQgHQRAEYRYiHARBEIRZSOG9\nNY7VyvVmYL/W+oB17PNAI9ANnAt8SWv9Woq5twKlQBdm343rtdZHlVJOzE5/QcweHv+ktX5qKT6P\nsLyYY3+9G3gHZs/5A8BdWuv/TjFX9lcBEc1BuAS4F0gu3VsOfEZr/deY3fm+OcfcceAvtdZfBx4C\nPmsdfxfg01rfDHweuEspJXXQ1yap9lcJcIPW+huYD/m/mWOu7K8CIsJhjaO1vpvpPbvRWv+V1toO\nY3NivqGlmvuVpHHbgVet/78deNIaMwhMAHtyvHRhBTDH/voXrXWb9WPyvpk5V/ZXARGzkjAnSikP\n8EHgk9bPLuAUcIHWusM6poDPAXXA9dbUeqY/EALWMUEAwOom+WXgMuB91jHZX8uIVSccFmlD3wz8\nFXAc2Az8L611cC3aOC3B8HfAjVrrEwBa65hSKvGHax3TwEeUUh8G/hVT5e8FKpIu57OOrXgWaUPf\njOwvALTWIeDzVqvgh5RSW7XWEdlfs/dX0rn3Af8GVGitZ2nzSqlq4FbgJLAD+KLWusc691nM35Mf\n+LXW+mcLrWU1mpUWY0P/e+B7lo3zFUx7JqwxG6f1Vvc94G+01s8ppf4o6XRR0rjPJh1vAbZa//85\npgPR3rDFwOG8LnrpWIwNXfYXoJS6Xill//7agVrM3yHI/kq1v1BKnQHsXmDu14DfaK1vBX4KfMua\nez5wudb6r4D/CdymlKpaaCGrTjhka0NXShUBlwPPWoeewLRtwiq2cSqlLgU+ADQppW6yBMO/AxcD\ndyilHgZusMa6gMeUUuut6QeUUrcopb6IaXr6S+v4j4BRpdT/hymIr9Fax5bsQ+WRbG3osr+m7S8v\n5t66AfMl5C+11gHZX6n3l1KqFNO09r8XmJ7YR0zfX1cztb8iwBHgDQutZdWZleZjPhs6EAdCSUIk\n2Y65am2cWutHgEdmHP7DOcbGgI1JP79rjnFxpt6K1wQL2dCR/ZXMLXOMlf2VmluAr2qtJ00XzBRK\nqWeBT2qtn2H6PgoAfqWU2zp+JGlaWvtr1WkOczGXDZ0p51c/UJKk7ibbMVetjVPIDVrrkNb685iC\n4SGlVJHsL2GxKKU2YvoJ3mVpWgCfUUqdZ/3/94GD1v+T95EPGNJaR8lyf60J4ZCODd1Stx7CdCiC\naVb5ufX/1WzjFBZJOjZ02V9CNmitT2utr9Va32r5EsB8jtkCoYgp/0RiHzF9f93H1P5yY/ouHl3o\n3quuKqtl47wGeAumpnAbpg19L9BpDSvTWh+YGTpnRZN8CdPbv4n/v307NkEgCKIw/LdgYCSYGCxW\nZmJkCwZiCZ6FWIGBWMGE2oSpwa4gzAkeZuf/wXQw8Jbdt/UR+9Um2VE/5cyB49jbJOr3Yb82wAy4\nA0vgHBGd+6Wh+vYrIh6llCmwArZtDm2nrsA6Ii7tYLEHbsCCWpJ4bytN2py+aSuNLhwkSb/7i2sl\nSdIwhoMkKTEcJEmJ4SBJSgwHSVJiOEiSEsNBkpQ8Aez9c4kP7hVIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111351470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eur_usd_resam['Mid'].plot(grid=True)\n",
    "# tag: eur_usd_resam\n",
    "# title: Resampled EUR/USD exchange rate tick data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "uuid": "d8d2f963-3909-4434-95fa-69f932b49451"
   },
   "outputs": [],
   "source": [
    "def reversal(x):\n",
    "    return 2 * 1.16 - x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "uuid": "e6d72fc4-0617-480c-84a3-56892f0e4b01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11133c6a0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sWdVZl7o7eiGSex3CK2s9iDjkiJXOmlqd9Vyf0djj3ddup6m2nMdf7OJU12jy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\nRhwAaB5Z/GUwtoqdkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1057ecf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eur_usd_resam['Mid'].apply(reversal).plot()\n",
    "# tag: aapl_resam_apply\n",
    "# title: Resampled EUR/USD exchange rate tick data with function applied to it"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "uuid": "ac44afa5-337a-4aed-b811-1b76db21682d"
   },
   "source": [
    "!rm ./data/*\n",
    "  # Windows: del /data/*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://python-for-finance.com\" target=\"_blank\">Python for Finance @ O'Reilly</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Listed Volatility and Variance Derivatives** |\n",
    "<a href=\"http://lvvd.tpq.io\" target=\"_blank\">Listed VV Derivatives @ Wiley Finance</a>\n",
    "\n",
    "**Python Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">Python for Finance University Certificate</a>"
   ]
  }
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